1. Field of the Invention
The present invention relates to an image processing device having a similar image searching function which considers layout features of an image document, and an image processing method for such an image processing device, and particularly, the present invention relates to a technique suitable for a multiple function peripheral, a file server, or an image processing program.
2. Description of the Related Art
It is well known that documents printed on paper can be transformed into digital data by using a scanner or other input devices. For example, in the related art, a device for electrical filing can be used for this purpose; but the electrical filing device is exclusively used for industrial purposes to process a large amount of paper documents.
In recent years, along with lowered prices of scanners, spreading usage of Multi-Function Peripherals (MFP), and progress in rulemaking on electrical documents, the electrical filing technique is widely accepted even in usual offices because of the good handing performance and convenience thereof, and it is becoming more and more common to use the electrical filing technique to transform paper documents into electrical data. In addition, it is becoming more and more common to store the electrical data of image documents in the form of a database (an image database) for management. For example, even when it is necessary to store the original paper documents, for purposes of easy management, usually people still construct databases.
Among the image document databases, there are large scale databases provided in a server device for access by many users, and small ones installed in personal computers for personal usage. The recent Multi-Function Peripherals have functions of storing documents in built-in hard disk drives (HDD), and thus, the image document databases can be installed in the Multi-Function Peripherals.
Some of the image document databases have searching functions allowing users to find a desired image document from the large amount of image documents. For example, currently, a frequently-used searching technique involves searching the whole text by using character recognition results given by an Optical Character Reader (OCR) process as keywords, or involves conceptual search.
However, the above-mentioned searching technique is text-based, and suffers from the following problems: (1) accuracy of the searching depends on the OCR accuracy; (2) keywords have to be used for searching; (3) when there are a large number of hits (namely, candidates), it is not easy to narrow the range of the hits.
As for problem (1), since presently it is not guaranteed that the characters recognized by OCR are 100% correct, if the input searching keywords, which are obtained by OCR, include incorrect characters due to misrecognition by OCR, the desired image document cannot be found.
As for problem (2), in the text-based searching, one has to use the keywords. When the user knows the appropriate keywords, there is not any inconvenience, but, for example, when searching for an object completely unknown to the user, such as a kind of Web site on the Internet, or when searching for a document created a few years ago, if the user has forgotten the appropriate keywords, the user cannot execute searching appropriately if he cannot think of good keywords.
Further, if the document only has pictures or graphics, but does not have text, certainly, searching with keywords is useless.
As for problem (3), in the text-based searching, it is difficult to rank the keywords, and candidates satisfying the keywords are treated equally. For this reason, when there are a large number of hits (candidates), one has to confirm the large number of hit image documents one by one, and this is quite cumbersome.
Among the methods for searching for image documents, there is a method involving searching for similar images, and an image classification method in which the image documents are classified into plural categories to gradually narrow the range of the image documents to be searched.
For example, Japanese Laid-Open Patent Application No. 2000-285141 (hereinafter, referred to as “reference 1”) discloses a similar image searching method. Specifically, reference 1 discloses an image searching method in which feature quantities of a query image are calculated from color, outline, or pattern, or other image attributes; then weight factors are assigned to the respective feature quantities, and similarities between the feature quantities and the query image are calculated and are ranked.
A problem in the similar image searching method is that the query image needs be used as a searching key. If an image at hand is used as the query image, there is not any convenience; but when using an image in an image database as the query image, it is necessary to first search for the query image, and this is not convenient.
The image classification method has good operability because one just needs to select a classified image category. For example, Japanese Laid-Open Patent Application No. 10-162020 (hereinafter, referred to as “reference 2”) discloses an invention in which the image classification method is applied to image documents. Specifically, in the invention disclosed in reference 2, images are classified into categories based on features extracted from an input image, and typical images of different categories are presented to the user. When the user selects an image, further, images of sub-categories are presented to the user. In this way, the range of the images is narrowed step by step, and the desired images can be determined with only a small number of steps.
However, in image classification of image documents having various attributes, in a classification sequence involving a uniquely defined and fixed classification key (feature quantity), sometimes the classification in the sub-category cannot be performed appropriately. Since the image documents have great variety, depending on the document type, sometimes image classification with image layouts is effective, and sometimes image classification with color or background color of the image is effective. For example, as for an image group of image documents each having white backgrounds, it is not appropriate to perform image classification with color; and for bills having the same format, it is not appropriate to perform image classification with image shape or layout.